{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['Arial Unicode MS']\n",
    "plt.rcParams[\"axes.unicode_minus\"] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 单因素方差分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构造4个行业被投诉次数数据集\n",
    "complaint_data = [\n",
    "    [57, 68, 31, 44],\n",
    "    [66, 39, 49, 51],\n",
    "    [49, 29, 21, 65],\n",
    "    [40, 45, 34, 77],\n",
    "    [34, 56, 40, 58],\n",
    "    [53, 51],\n",
    "    [44],\n",
    "]\n",
    "\n",
    "complaint_df = pd.DataFrame(complaint_data,\n",
    "                           columns=['零售业', '旅游业', '航空公司', '家电制造业'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>零售业</th>\n",
       "      <th>旅游业</th>\n",
       "      <th>航空公司</th>\n",
       "      <th>家电制造业</th>\n",
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       "      <td>68.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>44.0</td>\n",
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       "      <td>39.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>51.0</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>49</td>\n",
       "      <td>29.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>65.0</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>40</td>\n",
       "      <td>45.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>77.0</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>34</td>\n",
       "      <td>56.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>58.0</td>\n",
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       "      <td>51.0</td>\n",
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       "   零售业   旅游业  航空公司  家电制造业\n",
       "0   57  68.0  31.0   44.0\n",
       "1   66  39.0  49.0   51.0\n",
       "2   49  29.0  21.0   65.0\n",
       "3   40  45.0  34.0   77.0\n",
       "4   34  56.0  40.0   58.0\n",
       "5   53  51.0   NaN    NaN\n",
       "6   44   NaN   NaN    NaN"
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "complaint_df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "retail = [57, 66, 49, 40, 34, 53, 44]  # 零售业\n",
    "tourism = [68, 39, 29, 45, 56, 51]  # 旅游业\n",
    "airline = [31, 49, 21, 34, 40]  # 航空公司\n",
    "manufacturing = [44, 51, 65, 77, 58]  # 家电制造业"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [retail, tourism, airline, manufacturing]\n",
    "f, p = stats.f_oneway(*data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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   "source": [
    "f, p"
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   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
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   "source": [
    "# 绘制4个行业被投诉次数的散点图\n",
    "sns.scatterplot([0 for i in retail], retail)\n",
    "sns.scatterplot([1 for i in tourism], tourism)\n",
    "sns.scatterplot([2 for i in airline], airline)\n",
    "sns.scatterplot([3 for i in manufacturing], manufacturing)\n",
    "\n",
    "# 绘制每个行业投诉次数均值折线图\n",
    "x = [i for i in range(4)]\n",
    "y = [np.mean(retail), np.mean(tourism), np.mean(airline), np.mean(manufacturing)]\n",
    "sns.lineplot(x, y, marker=\"o\")\n",
    "\n",
    "# 添加x轴标签\n",
    "plt.gca().set_xticks(x)\n",
    "plt.gca().set_xticklabels(['零售业', '旅游业', '航空公司', '家电制造业'])"
   ]
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